用合成人脸数据提升单形态攻击检测，但需谨慎控制数量
Exploration of Incremental Synthetic Non-Morphed Images for Single Morphing Attack Detection
- 逐步引入可控数量的合成非变形图像增强模型泛化能力
- 仅用合成数据时错误率最低，但整体性能不如混合数据
- 适合需要隐私保护且数据稀缺场景的生物识别系统优化
本文研究利用合成人脸数据提升单形态攻击检测（S-MAD）性能，以应对真实人脸数据因隐私问题导致的大规模可用性不足。通过多种形态工具和跨数据集评估方案开展实验，采用增量测试协议评估随着合成图像增加模型的泛化能力。结果表明，合理引入适量合成图像或在训练中渐进添加真实图像可提升泛化性能；但盲目使用合成数据可能导致次优表现。值得注意的是，仅使用合成数据（包括变形与非变形图像）可达到最低等错误率（EER），但在实际应用中，最佳方案并非完全依赖合成数据。
原文摘要 · Abstract (English)
This paper investigates the use of synthetic face data to enhance Single-Morphing Attack Detection (S-MAD), addressing the limitations of availability of large-scale datasets of bona fide images due to privacy concerns. Various morphing tools and cross-dataset evaluation schemes were utilized to conduct this study. An incremental testing protocol was implemented to assess the generalization capabilities as more and more synthetic images were added. The results of the experiments show that generalization can be improved by carefully incorporating a controlled number of synthetic images into existing datasets or by gradually adding bona fide images during training. However, indiscriminate use of synthetic data can lead to sub-optimal performance. Evenmore, the use of only synthetic data (morphed and non-morphed images) achieves the highest Equal Error Rate (EER), which means in operational scenarios the best option is not relying only on synthetic data for S-MAD.
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